EQNet: A Post-Processing Approach to Manage Popularity Bias in Collaborative Filter Recommender Systems

Abstract

Recommendation systems play a pivotal role in digital platforms, facilitating novel user experiences by effectively sorting and presenting items that align with their preferences. However, these systems often suffer from popularity bias, a phenomenon characterized by the algorithm’s inclination to favor a few popular items, resulting in the under-representation of the vast majority of items. Addressing this bias and enhancing the recommendation of long-tail items is of utmost importance. In this paper, we propose the EQNet, a re-ranking approach designed to mitigate popularity bias and improve the recommendation quality of an SVD-based recommendation system. EQNet leverages PageRank or Popularity Count outputs to re-rank items, and its effectiveness is evaluated using four metrics: average popularity, percentage of long-tailed items, coverage of long-tailed items, and recommendation quality. We incorporate the widely recognized bias mitigation algorithm FA*IR into our experimentation to establish a robust baseline. By comparing the performance of EQNet against this state-of-the-art approach, we show the efficiency of EQNet and highlight its potential to enhance existing methods for mitigating popularity bias.